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Identification of forearm skin zones with similar muscle activation patterns during activities of daily living

BACKGROUND: A deeper knowledge of the activity of the forearm muscles during activities of daily living (ADL) could help to better understand the role of those muscles and allow clinicians to treat motor dysfunctions more effectively and thus improve patients’ ability to perform activities of daily...

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Autores principales: Jarque-Bou, Néstor J., Vergara, Margarita, Sancho-Bru, Joaquín L., Roda-Sales, Alba, Gracia-Ibáñez, Verónica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206932/
https://www.ncbi.nlm.nih.gov/pubmed/30373606
http://dx.doi.org/10.1186/s12984-018-0437-0
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author Jarque-Bou, Néstor J.
Vergara, Margarita
Sancho-Bru, Joaquín L.
Roda-Sales, Alba
Gracia-Ibáñez, Verónica
author_facet Jarque-Bou, Néstor J.
Vergara, Margarita
Sancho-Bru, Joaquín L.
Roda-Sales, Alba
Gracia-Ibáñez, Verónica
author_sort Jarque-Bou, Néstor J.
collection PubMed
description BACKGROUND: A deeper knowledge of the activity of the forearm muscles during activities of daily living (ADL) could help to better understand the role of those muscles and allow clinicians to treat motor dysfunctions more effectively and thus improve patients’ ability to perform activities of daily living. METHODS: In this work, we recorded sEMG activity from 30 spots distributed over the skin of the whole forearm of six subjects during the performance of 21 representative simulated ADL from the Sollerman Hand Function Test. Functional principal component analysis and hierarchical cluster analysis (HCA) were used to identify forearm spots with similar muscle activation patterns. RESULTS: The best classification of spots with similar activity in simulated ADL consisted in seven muscular-anatomically coherent groups: (1) wrist flexion and ulnar deviation; (2) wrist flexion and radial deviation; (3) digit flexion; (4) thumb extension and abduction/adduction; (5) finger extension; (6) wrist extension and ulnar deviation; and (7) wrist extension and radial deviation. CONCLUSION: The number of sEMG sensors could be reduced from 30 to 7 without losing any relevant information, using them as representative spots of the muscular activity of the forearm in simulated ADL. This may help to assess muscle function in rehabilitation while also simplifying the complexity of prosthesis control.
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spelling pubmed-62069322018-11-16 Identification of forearm skin zones with similar muscle activation patterns during activities of daily living Jarque-Bou, Néstor J. Vergara, Margarita Sancho-Bru, Joaquín L. Roda-Sales, Alba Gracia-Ibáñez, Verónica J Neuroeng Rehabil Research BACKGROUND: A deeper knowledge of the activity of the forearm muscles during activities of daily living (ADL) could help to better understand the role of those muscles and allow clinicians to treat motor dysfunctions more effectively and thus improve patients’ ability to perform activities of daily living. METHODS: In this work, we recorded sEMG activity from 30 spots distributed over the skin of the whole forearm of six subjects during the performance of 21 representative simulated ADL from the Sollerman Hand Function Test. Functional principal component analysis and hierarchical cluster analysis (HCA) were used to identify forearm spots with similar muscle activation patterns. RESULTS: The best classification of spots with similar activity in simulated ADL consisted in seven muscular-anatomically coherent groups: (1) wrist flexion and ulnar deviation; (2) wrist flexion and radial deviation; (3) digit flexion; (4) thumb extension and abduction/adduction; (5) finger extension; (6) wrist extension and ulnar deviation; and (7) wrist extension and radial deviation. CONCLUSION: The number of sEMG sensors could be reduced from 30 to 7 without losing any relevant information, using them as representative spots of the muscular activity of the forearm in simulated ADL. This may help to assess muscle function in rehabilitation while also simplifying the complexity of prosthesis control. BioMed Central 2018-10-29 /pmc/articles/PMC6206932/ /pubmed/30373606 http://dx.doi.org/10.1186/s12984-018-0437-0 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Jarque-Bou, Néstor J.
Vergara, Margarita
Sancho-Bru, Joaquín L.
Roda-Sales, Alba
Gracia-Ibáñez, Verónica
Identification of forearm skin zones with similar muscle activation patterns during activities of daily living
title Identification of forearm skin zones with similar muscle activation patterns during activities of daily living
title_full Identification of forearm skin zones with similar muscle activation patterns during activities of daily living
title_fullStr Identification of forearm skin zones with similar muscle activation patterns during activities of daily living
title_full_unstemmed Identification of forearm skin zones with similar muscle activation patterns during activities of daily living
title_short Identification of forearm skin zones with similar muscle activation patterns during activities of daily living
title_sort identification of forearm skin zones with similar muscle activation patterns during activities of daily living
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206932/
https://www.ncbi.nlm.nih.gov/pubmed/30373606
http://dx.doi.org/10.1186/s12984-018-0437-0
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